构建用于基于结构的分子间结合亲和力预测的机器学习评分函数。
Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.
作者信息
Wójcikowski Maciej, Siedlecki Pawel, Ballester Pedro J
机构信息
Institute of Biochemistry and Biophysics PAS, Warsaw, Poland.
Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Warsaw, Poland.
出版信息
Methods Mol Biol. 2019;2053:1-12. doi: 10.1007/978-1-4939-9752-7_1.
Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to build RF-Score, a scoring function utilizing the machine-learning technique known as Random Forest (RF). We also point out how to use different data, features, and regression models using either R or Python programming languages.
分子对接能够大规模预测小分子是否以及如何与大分子靶点结合。机器学习评分函数特别适合预测这种相互作用的强度。在此，我们描述如何构建RF-Score，这是一种利用称为随机森林（RF）的机器学习技术的评分函数。我们还指出如何使用R或Python编程语言，利用不同的数据、特征和回归模型。
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